Supported languages: Assamese, Bengali, Gujarati, Hindi, Marathi, Odiya, Punjabi, Kannada, Malayalam, Tamil, and Telugu. Not all of these languages are supported by mBART50 and mT5.
The model is much smaller than the mBART and mT5(-base) models, so less computationally expensive for decoding.
Trained on large Indic language corpora (5.53 million sentences).
Unlike
MultiIndicSentenceSummarization
each language is written in its own script, so you do not need to perform any script mapping to/from Devanagari.
Using this model in
transformers
from transformers import MBartForConditionalGeneration, AutoModelForSeq2SeqLM
from transformers import AlbertTokenizer, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("ai4bharat/MultiIndicSentenceSummarizationSS", do_lower_case=False, use_fast=False, keep_accents=True)
# Or use tokenizer = AlbertTokenizer.from_pretrained("ai4bharat/MultiIndicSentenceSummarizationSS", do_lower_case=False, use_fast=False, keep_accents=True)
model = AutoModelForSeq2SeqLM.from_pretrained("ai4bharat/MultiIndicSentenceSummarizationSS")
# Or use model = MBartForConditionalGeneration.from_pretrained("ai4bharat/MultiIndicSentenceSummarizationSS")
# Some initial mapping
bos_id = tokenizer._convert_token_to_id_with_added_voc("<s>")
eos_id = tokenizer._convert_token_to_id_with_added_voc("</s>")
pad_id = tokenizer._convert_token_to_id_with_added_voc("<pad>")
# To get lang_id use any of ['<2as>', '<2bn>', '<2en>', '<2gu>', '<2hi>', '<2kn>', '<2ml>', '<2mr>', '<2or>', '<2pa>', '<2ta>', '<2te>']
# First tokenize the input. The format below is how IndicBART was trained so the input should be "Sentence </s> <2xx>" where xx is the language code. Similarly, the output should be "<2yy> Sentence </s>".
inp = tokenizer("जम्मू एवं कश्मीर के अनंतनाग जिले में शनिवार को सुरक्षाबलों के साथ मुठभेड़ में दो आतंकवादियों को मार गिराया गया। </s> <2hi>", add_special_tokens=False, return_tensors="pt", padding=True).input_ids
# For generation. Pardon the messiness. Note the decoder_start_token_id.
model_output=model.generate(inp, use_cache=True,no_repeat_ngram_size=3, num_beams=5, length_penalty=0.8, max_length=20, min_length=1, early_stopping=True, pad_token_id=pad_id, bos_token_id=bos_id, eos_token_id=eos_id, decoder_start_token_id=tokenizer._convert_token_to_id_with_added_voc("<2hi>"))
# Decode to get output strings
decoded_output=tokenizer.decode(model_output[0], skip_special_tokens=True, clean_up_tokenization_spaces=False)
print(decoded_output) # अनंतनाग में सुरक्षाबलों के साथ मुठभेड़ में दो आतंकवादी ढेर
Benchmarks
Scores on the
IndicSentenceSummarization
test sets are as follows:
Language
Rouge-1 / Rouge-2 / Rouge-L
as
63.56 / 49.90 / 62.57
bn
52.52 / 36.15 / 50.60
gu
47.69 / 29.77 / 45.61
hi
50.43 / 28.13 / 45.15
kn
77.06 / 69.36 / 76.33
ml
65.00 / 51.99 / 63.76
mr
47.05 / 25.97 / 45.52
or
50.96 / 30.32 / 49.23
pa
54.95 / 36.26 / 51.26
ta
58.52 / 38.36 / 56.49
te
53.75 / 35.17 / 52.66
Citation
If you use this model, please cite the following paper:
@inproceedings{Kumar2022IndicNLGSM,
title={IndicNLG Suite: Multilingual Datasets for Diverse NLG Tasks in Indic Languages},
author={Aman Kumar and Himani Shrotriya and Prachi Sahu and Raj Dabre and Ratish Puduppully and Anoop Kunchukuttan and Amogh Mishra and Mitesh M. Khapra and Pratyush Kumar},
year={2022},
url = "https://arxiv.org/abs/2203.05437"
}
Runs of ai4bharat MultiIndicSentenceSummarizationSS on huggingface.co
15
Total runs
2
24-hour runs
2
3-day runs
0
7-day runs
4
30-day runs
More Information About MultiIndicSentenceSummarizationSS huggingface.co Model
More MultiIndicSentenceSummarizationSS license Visit here:
MultiIndicSentenceSummarizationSS huggingface.co is an AI model on huggingface.co that provides MultiIndicSentenceSummarizationSS's model effect (), which can be used instantly with this ai4bharat MultiIndicSentenceSummarizationSS model. huggingface.co supports a free trial of the MultiIndicSentenceSummarizationSS model, and also provides paid use of the MultiIndicSentenceSummarizationSS. Support call MultiIndicSentenceSummarizationSS model through api, including Node.js, Python, http.
MultiIndicSentenceSummarizationSS huggingface.co is an online trial and call api platform, which integrates MultiIndicSentenceSummarizationSS's modeling effects, including api services, and provides a free online trial of MultiIndicSentenceSummarizationSS, you can try MultiIndicSentenceSummarizationSS online for free by clicking the link below.
ai4bharat MultiIndicSentenceSummarizationSS online free url in huggingface.co:
MultiIndicSentenceSummarizationSS is an open source model from GitHub that offers a free installation service, and any user can find MultiIndicSentenceSummarizationSS on GitHub to install. At the same time, huggingface.co provides the effect of MultiIndicSentenceSummarizationSS install, users can directly use MultiIndicSentenceSummarizationSS installed effect in huggingface.co for debugging and trial. It also supports api for free installation.
MultiIndicSentenceSummarizationSS install url in huggingface.co: